Bayesian optimization algorithms for accelerator physics
Accelerator physics relies on numerical algorithms to solve optimization problems in online
accelerator control and tasks such as experimental design and model calibration in …
accelerator control and tasks such as experimental design and model calibration in …
Large language models for human-machine collaborative particle accelerator tuning through natural language
Autonomous tuning of particle accelerators is an active and challenging research field with
the goal of enabling advanced accelerator technologies and cutting-edge high-impact …
the goal of enabling advanced accelerator technologies and cutting-edge high-impact …
Learning to do or learning while doing: Reinforcement learning and bayesian optimisation for online continuous tuning
Online tuning of real-world plants is a complex optimisation problem that continues to
require manual intervention by experienced human operators. Autonomous tuning is a …
require manual intervention by experienced human operators. Autonomous tuning is a …
Bridging the gap between machine learning and particle accelerator physics with high-speed, differentiable simulations
Machine learning has emerged as a powerful solution to the modern challenges in
accelerator physics. However, the limited availability of beam time, the computational cost of …
accelerator physics. However, the limited availability of beam time, the computational cost of …
[PDF][PDF] Optimisation of the Touschek Lifetime in Synchrotron Light Sources Using Badger
Badger [1] is a software designed to easily access several optimizers (simplex, RCDS [2],
Bayesian optimization, etc.) to solve a given multidimensional minimization/maximization …
Bayesian optimization, etc.) to solve a given multidimensional minimization/maximization …
Towards Agentic AI on Particle Accelerators
As particle accelerators grow in complexity, traditional control methods face increasing
challenges in achieving optimal performance. This paper envisions a paradigm shift: a …
challenges in achieving optimal performance. This paper envisions a paradigm shift: a …
[PDF][PDF] Bayesian Optimization for SASE Tuning at the European XFEL
Parameter tuning is a regular task and takes considerable time for daily operations at FEL
facilities. In this contribution, we demonstrate SASE pulse energy optimization at the …
facilities. In this contribution, we demonstrate SASE pulse energy optimization at the …
[PDF][PDF] How can machine learning help future light sources?
Abstract Machine learning (ML) is one of the key technologies that can considerably extend
and advance the capabilities of particle accelerators and needs to be included in their future …
and advance the capabilities of particle accelerators and needs to be included in their future …
[PDF][PDF] Integration of an Optimizer Framework into the Control System at KARA
Tuning particle accelerators is not straightforward as they depend on a large number of non-
linearly correlated parameters that drift over time. In recent years advanced numerical …
linearly correlated parameters that drift over time. In recent years advanced numerical …
[PDF][PDF] Vertical beam halo characterisation at the ESRF EBS for operation with reduced in vacuum undulator gap
The vertical beam halo is the main limitation for very low gap operation of in-vacuum
undulators at the ESRF EBS. The vertical halo is due to Touschek electrons with large …
undulators at the ESRF EBS. The vertical halo is due to Touschek electrons with large …